Towards a universal representation for audio information retrieval and analysis
| Autor: | Rasmus Troelsgaard, Jan Larsen, Lars Kai Hansen, Bjørn Sand Jensen |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Audio mining
Information retrieval business.industry Computer science Representation (systemics) Lyrics computer.software_genre Speech processing Latent Dirichlet allocation symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Categorization Benchmark (computing) symbols Artificial intelligence business Joint (audio engineering) Audio signal processing computer Natural language processing |
| Zdroj: | ICASSP |
| Popis: | A fundamental and general representation of audio and music which integrates multi-modal data sources is important for both application and basic research purposes. In this paper we address this challenge by proposing a multi-modal version of the Latent Dirichlet Allocation model which provides a joint latent representation. We evaluate this representation on the Million Song Dataset by integrating three fundamentally different modalities, namely tags, lyrics, and audio features. We show how the resulting representation is aligned with common 'cognitive' variables such as tags, and provide some evidence for the common assumption that genres form an acceptable categorization when evaluating latent representations of music. We furthermore quantify the model by its predictive performance in terms of genre and style, providing benchmark results for the Million Song Dataset. |
| Databáze: | OpenAIRE |
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